class_docstr: Shards sequence (unpooled) embedding data-parallel, with no table sharding i.e.. a
  given embedding table is replicated across all ranks.
class_docstr_tok: ["Shards","sequence","(","unpooled",")","embedding","data-parallel",",","with","no","table","sharding","i.e","..","a","given","embedding","table","is","replicated","across","all","ranks","."]
code: def create_output_dist(
      self, device: Optional[torch.device] = None
  ) -> BaseEmbeddingDist[
      SequenceShardingContext, torch.Tensor, torch.Tensor
  ]:
      return DpSequenceEmbeddingDist()
  
file_path: torchrec/torchrec/distributed/sharding/dp_sequence_sharding.py
imports: from typing import Any, Dict, Optional
  import torch
  from torchrec.distributed.embedding_lookup import GroupedEmbeddingsLookup
  from torchrec.distributed.embedding_sharding import BaseEmbeddingDist, BaseEmbeddingLookup, BaseSparseFeaturesDist
  from torchrec.distributed.embedding_types import BaseGroupedFeatureProcessor
  from torchrec.distributed.sharding.dp_sharding import BaseDpEmbeddingSharding, DpSparseFeaturesDist
  from torchrec.distributed.sharding.sequence_sharding import SequenceShardingContext
  from torchrec.distributed.types import Awaitable, NoWait
  from torchrec.sparse.jagged_tensor import KeyedJaggedTensor
label_desc: Use image node_id 3 for calling the DpSequenceEmbeddingSharding obj's underlying member method code with example usage: obj.create_output_dist(device) and returns: DpSequenceEmbeddingDist